If AI ambition inside your bank is starting to collide with day‑to‑day delivery realities, this six‑minute video is worth your time.
It’s a “cut through the noise” conversation about what’s shaping banks right now: risks, opportunities and the reality behind the hype.
AI’s impact depends on the foundation
Listen to my SAS colleagues Diana Rothfuss, Director, Global Industry Solutions - Financial Services and Julie Muckleroy, Global Banking Market Strategy Advisor, in the first episode of the enlightening Brewing Curiosity video series. Rather than covering everything, the discussion stays focused on a single, timely premise: the biggest blocker to AI’s impact in banking is the foundation.
Legacy spend, board pressure and proving value
According to Accenture, banks now spend about 70% of their IT budgets on maintaining legacy systems. As a result, pivoting to AI – and proving value quickly – remains so difficult.
In that context, Brewing Curiosity: Banking Unfiltered is practical and executive-friendly. The dialogue quickly acknowledges the industry’s resilience, profitability and performance while emphasizing the nuances underneath: shifting risk profiles, customer expectations, increased scrutiny and boards that want evidence, not endless pilots.
The language is crisp and quotable without being sensational, especially the central idea that AI can’t scale on “shaky foundations.” That line isn’t used as a slogan; instead, it’s used as a thesis that the rest of the discussion keeps reinforcing.
Three key takeaways
- AI adoption is stalling for operational reasons, not just for strategic ones. The video connects stalled momentum to the realities of governance, risk, and delivery. The subtext is clear – experimentation was easy. Operationalizing AI across lines of business is the hard part.
- Data trust is the real constraint. Weak lineage, inconsistent quality, fragmented systems and more access without greater clarity lead to slower onboarding, uneven risk decisions and difficulty proving ROI. It’s no longer just “garbage in, garbage out.” As we often say at SAS, “garbage in, garbage scaled.”
- 2026 is the maturity pivot: from pilots to production. The discussion makes the case that banks don’t need hundreds of disconnected models – they need governed, explainable decisions that can be defended and understood by the business unit. In other words, orchestration and accountability matter as much as algorithms.
Go deeper with two supporting reports
Think of the video as a concise executive briefing, with two supporting reports that help go deeper. First, the SAS 2026 Banking Trends report provides a broader industry context and a structured view of what’s changing and why. It’s a helpful baseline for aligning stakeholders on priorities and the external pressures driving them.
Second, the SAS Data and AI Impact Report is a strong complement when you need language and examples for outcomes – such as how organizations are translating data and AI investments into measurable results and where gaps persist.
Moving from AI curiosity to AI capability
Watch the first episode of Brewing Curiosity not to capture every point, but to listen for the vocabulary it gives you to have better internal conversations about things that matter, including foundation, trust, governance, explainability and orchestration.
This video lays the groundwork for the rest of the series, supported by the two accompanying reports. Use those resources to go deeper with different audiences – trends for leadership alignment and impact for teams that need to tie investments to real outcomes. For anyone trying to pivot from “AI curiosity” to “AI capability” in 2026, this episode is a concise and grounded starting point.
